



import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


a      :  Union[str, Any]			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		nn.ModuleList		,						lowerCAmelCase__:		nn.ModuleList		,						lowerCAmelCase__:		List[int]  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[Any]							      =     nn.ModuleList([src_layers[i] for i in layers_to_copy]  )
			assert len(lowerCAmelCase__  ) == len(lowerCAmelCase__  ), F'{len(lowerCAmelCase__  )} != {len(lowerCAmelCase__  )}'
			dest_layers.load_state_dict(layers_to_copy.state_dict()  )


a      :  Union[str, Any]			     =			{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
a      :  Union[str, Any]			     =			{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}







def  lowerCAmelCase_	(lowerCAmelCase__:		Union[str, Any]		,						lowerCAmelCase__:		Optional[int]  ):





			"""simple docstring"""


			try:
						UpperCAmelCase_:		Dict							      =     LAYERS_TO_COPY[n_teacher][n_student]
						return val
			except KeyError:
						if n_student != n_teacher:
									warnings.warn(
									    F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
									    F' {n_student}'  )
						return list(range(lowerCAmelCase__  )  )







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			if n_student > n_teacher:
						raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'  )
			elif n_teacher == n_student:
						return list(range(lowerCAmelCase__  )  )
			elif n_student == 1:
						return [n_teacher - 1]
			else:
						return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def  lowerCAmelCase_	(lowerCAmelCase__:		Union[str, PreTrainedModel]		,						lowerCAmelCase__:		Union[str, Path] = "student"		,						lowerCAmelCase__:		Union[int, None] = None		,						lowerCAmelCase__:		Union[int, None] = None		,						lowerCAmelCase__:		Optional[int]=False		,						lowerCAmelCase__:		List[Any]=None		,						lowerCAmelCase__:		Optional[int]=None		,						**lowerCAmelCase__:		Optional[int]		,						):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
			assert (e is not None) or (d is not None), _msg
			if isinstance(lowerCAmelCase__		,						lowerCAmelCase__  ):
						AutoTokenizer.from_pretrained(lowerCAmelCase__  ).save_pretrained(lowerCAmelCase__  )  # purely for convenience
						UpperCAmelCase_:		int							      =     AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__  ).eval()
			else:
						assert isinstance(lowerCAmelCase__		,						lowerCAmelCase__  ), F'teacher must be a model or string got type {type(lowerCAmelCase__  )}'
			UpperCAmelCase_:		Any							      =     teacher.config.to_diff_dict()

			try:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     teacher.config.encoder_layers, teacher.config.decoder_layers
						if e is None:
									UpperCAmelCase_:		str							      =     teacher_e
						if d is None:
									UpperCAmelCase_:		Dict							      =     teacher_d
						init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d}  )
			except AttributeError:  # T5
						if hasattr(teacher.config		,						"""num_encoder_layers"""  ):
									UpperCAmelCase_      ,							UpperCAmelCase_:		str							      =     teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
						else:
									UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     teacher.config.num_layers, teacher.config.num_decoder_layers
						if e is None:
									UpperCAmelCase_:		int							      =     teacher_e
						if d is None:
									UpperCAmelCase_:		List[Any]							      =     teacher_d
						if hasattr(teacher.config		,						"""num_encoder_layers"""  ):
									init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d}  )
						else:
									init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d}  )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
			init_kwargs.update(lowerCAmelCase__  )

			# Copy weights
			UpperCAmelCase_:		Any							      =     teacher.config_class(**lowerCAmelCase__  )
			UpperCAmelCase_:		Optional[int]							      =     AutoModelForSeqaSeqLM.from_config(lowerCAmelCase__  )
			# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
			UpperCAmelCase_:		Tuple							      =     student.load_state_dict(teacher.state_dict()		,						strict=lowerCAmelCase__  )
			assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

			if copy_first_teacher_layers:  # Our copying is done. We just log and save
						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     list(range(lowerCAmelCase__  )  ), list(range(lowerCAmelCase__  )  )
						logger.info(
						    F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
						    F' {save_path}'  )
						student.save_pretrained(lowerCAmelCase__  )
						return student, e_layers_to_copy, d_layers_to_copy

			# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
			if e_layers_to_copy is None:
						UpperCAmelCase_:		List[int]							      =     pick_layers_to_copy(lowerCAmelCase__		,						lowerCAmelCase__  )
			if d_layers_to_copy is None:
						UpperCAmelCase_:		List[int]							      =     pick_layers_to_copy(lowerCAmelCase__		,						lowerCAmelCase__  )

			try:
						if hasattr(
						    lowerCAmelCase__		,						"""prophetnet"""  ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
									copy_layers(teacher.prophetnet.encoder.layers		,						student.prophetnet.encoder.layers		,						lowerCAmelCase__  )
									copy_layers(teacher.prophetnet.decoder.layers		,						student.prophetnet.decoder.layers		,						lowerCAmelCase__  )
						else:
									copy_layers(teacher.model.encoder.layers		,						student.model.encoder.layers		,						lowerCAmelCase__  )
									copy_layers(teacher.model.decoder.layers		,						student.model.decoder.layers		,						lowerCAmelCase__  )
			except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
						copy_layers(teacher.encoder.block		,						student.encoder.block		,						lowerCAmelCase__  )
						copy_layers(teacher.decoder.block		,						student.decoder.block		,						lowerCAmelCase__  )
			logger.info(
			    F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'  )
			UpperCAmelCase_:		Optional[Any]							      =     {
			    """teacher_type""": teacher.config.model_type,
			    """copied_encoder_layers""": e_layers_to_copy,
			    """copied_decoder_layers""": d_layers_to_copy,
			}
			student.save_pretrained(lowerCAmelCase__  )
			# Save information about copying for easier reproducibility

			return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
	fire.Fire(create_student_by_copying_alternating_layers)











from __future__ import annotations







def  lowerCAmelCase_	(lowerCAmelCase__:		int | str  ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     str(lowerCAmelCase__  )
			return n == n[::-1]







def  lowerCAmelCase_	(lowerCAmelCase__:		int = 1_0_0_0_0_0_0  ):





			"""simple docstring"""


			UpperCAmelCase_:		int							      =     0

			for i in range(1		,						lowerCAmelCase__  ):
						if is_palindrome(lowerCAmelCase__  ) and is_palindrome(bin(lowerCAmelCase__  ).split("""b"""  )[1]  ):
									total += i
			return total


if __name__ == "__main__":
	print(solution(int(str(input().strip()))))






